# Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2102.00280 · HTML: https://mlsanity.com/paper/2102.00280/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Sherpa: Robust Hyperparameter Optimization for Machine Learning](https://mlsanity.com/paper/2005.04048/) (2020)
- [A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation](https://mlsanity.com/paper/1506.06472/) (2015)
- [Learning in the Machine: To Share or Not to Share?](https://mlsanity.com/paper/1909.11483/) (2019)
- [Automated pulmonary nodule detection using 3D deep convolutional neural networks](https://mlsanity.com/paper/1903.09876/) (2019)

## Abstract (arXiv)

Coccidioidomycosis is the most common systemic mycosis in dogs in the southwestern United States. With warming climates, affected areas and number of cases are expected to increase in the coming years, escalating also the chances of transmission to humans. As a result, developing methods for automating the detection of the disease is important, as this will help doctors and veterinarians more easily identify and diagnose positive cases. We apply machine learning models to provide accurate and interpretable predictions of Coccidioidomycosis. We assemble a set of radiographic images and use it to train and test state-of-the-art convolutional neural networks to detect Coccidioidomycosis. These methods are relatively inexpensive to train and very fast at inference time. We demonstrate the successful application of this approach to detect the disease with an Area Under the Curve (AUC) above 0.99 using 10-fold cross validation. We also use the classification model to identify regions of interest and localize the disease in the radiographic images, as illustrated through visual heatmaps. This proof-of-concept study establishes the feasibility of very accurate and rapid automated detection of Valley Fever in radiographic images.